Information retrieval (IR) and IR systems provide access to books, journals, and other documents, and websites (web pages) on the world wide web. Examples of IR systems include Microsoft® Live Search and Google® Search. The IR systems may also be implemented in smaller networks or on personal computers, for example, many universities and public libraries that provide access to books, journals, and other documents. The IR systems typically have two main tasks, that is, to find relevant documents related to a user query and to rank these documents according to their relevance to the user query.
In IR and related fields, learning to rank methods have gained increased attention for better presentation of the retrieved information. In a generic learning to rank method, machine learning techniques are used to rank documents according to their relevance to the query. In machine learning technique, ranking is performed by means of classification of instance or document pairs. Each document pair consists of two documents from two different ranks. Therefore, for each document pairs, there is an order between the two documents. A classification is performed for identifying the order relationship between the two documents in any document pair. The ranking of documents can be then conducted based on the classification model. However, such ranking methods are not sufficient to rank order relationships. Accordingly, there remains a need to improve ranking methods for information retrieval technology.